Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Summary

Hyperspectral Reflectance Imaging hypercubes include remarkable information into a large amount of data. Therefore, the request for automated protocols to manage and study the datasets is widely justified. The combination of Spectral Angle Mapper, data manipulation, and a user-adjustable analysis method constitutes a key-turn for exploring the experimental results.

Abstract

Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are well-established techniques for the investigation of works of art with particular attention to paintings. Most modern museums put at the disposal of their research groups portable equipment that, together with the intrinsic non-invasiveness of RS and FORS, makes possible the in situ collection of reflectance spectra from the surface of artefacts. The comparison, performed by experts in pigments and painting materials, of the experimental data with databases of reference spectra drives the characterization of the palettes and of the techniques used by the artists. However, this approach requires specific skills and it is time consuming especially if the number of the spectra to be investigated becomes large as is the case of Hyperspectral Reflectance Imaging (HRI) datasets. The HRI experimental setups are multi-dimensional cameras that associate the spectral information, given by the reflectance spectra, with the spatial localization of the spectra over the painted surface. The resulting datasets are 3D-cubes (called hypercubes or data-cubes) where the first two dimensions locate the spectrum over the painting and the third is the spectrum itself (i.e., the reflectance of that point of the painted surface versus the wavelength in the operative range of the detector). The capability of the detector to simultaneously collect a great number of spectra (typically much more than 10,000 for each hypercube) makes the HRI datasets large reservoirs of information and justifies the need for the development of robust and, possibly, automated protocols to analyze the data. After the description of the procedure designed for the data acquisition, we present an analysis method that systematically exploits the potential of the hypercubes. Based on Spectral Angle Mapper (SAM) and on the manipulation of the collected spectra, the algorithm handles and analyzes thousands of spectra while at the same time it supports the user to unveil the features of the samples under investigation. The power of the approach is illustrated by applying it to Quarto Stato, the iconic masterpiece by Giuseppe Pellizza da Volpedo, held in the Museo del Novecento in Milan (Italy).

Introduction

Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are based on the detection of the light reflected by surfaces once illuminated by a light source, typically a tungsten-halogen lamp. The output of the acquisition system is constituted by spectra where the reflectance is monitored as a function of the wavelength in a range that depends on the characteristics of the employed experimental setup^1,2,3. Introduced during the last four decades^4,5, RS and FORS are typically used in combination wit....

Protocol

1. Set the spatial resolution of the hypercubes

1. Perform a preliminary inspection of the painted surface (Figure 1) supported by art experts to identify the main features of the painting.
   1. Recognize the pictorial techniques employed by the artist to create the painting.
   2. Identify the different brush strokes of paint on the canvas.
   3. Estimate, qualitatively, the characteristics of the brush strokes with particular attention to their size.
2. Mimic the pictorial technique used by the artist by creating ad-hoc test samples where the brush strokes show characteristics simi....

Results

The proposed protocol offers a set of interesting features for the management and the analysis of HRI data. The I/O (step 3.1) of the raw data is always the first problem that must be solved before applying any analysis method and it can become a critical issue when dealing with large amounts of data. In the present case, the only task regarding the raw data is to store the experimental results into a dedicated folder and select it by browsing the hard disk when running the reading code (step 3.1.1). Thereafter, the crop.......

Discussion

Hyperspectral reflectance imaging datasets are large reservoirs of information; therefore, the development of robust and, possibly, automated protocols to analyze the data is a key turn to exploit their potential^15,17. The proposed algorithm answers this need in the field of cultural heritage with particular attention to the characterization of the pigments of paintings. Based on SAM^20,21, the algorithm s.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
ImageJ/Fiji	Specim (Oulo, Finlad)	N/A	Portable reflectance hyperspectral camera used to acquire the hypercubes
MATLAB 2019b	StellarNet Inc (Tampa, Florida, USA)	N/A	Portable reflectance spectrometer used to acquire independent reflectance spectra
Specim IQ Hyperspectral Camera	National Institutes of Health (Bethesda, Maryland, USA)	N/A	Open source Java image processing program
StellarNet BLUE-wave Miniature Spectrometer	MathWorks (Natick, Massachusset, USA)	N/A	Program Language and numerical computing environment